Skip to content

json_schema

The json_schema module contains classes and functions to allow the way JSON Schema is generated to be customized.

In general you shouldn't need to use this module directly; instead, you can BaseModel.model_json_schema and TypeAdapter.json_schema.

CoreSchemaOrFieldType module-attribute

CoreSchemaOrFieldType = Literal[
    core_schema.CoreSchemaType,
    core_schema.CoreSchemaFieldType,
]

A type alias for defined schema types that represents a union of core_schema.CoreSchemaType and core_schema.CoreSchemaFieldType.

JsonSchemaValue module-attribute

JsonSchemaValue = Dict[str, Any]

A type alias for a JSON schema value. This is a dictionary of string keys to arbitrary values.

JsonSchemaMode module-attribute

JsonSchemaMode = Literal['validation', 'serialization']

A type alias that represents the mode of a JSON schema; either 'validation' or 'serialization'.

For some types, the inputs to validation differ from the outputs of serialization. For example, computed fields will only be present when serializing, and should not be provided when validating. This flag provides a way to indicate whether you want the JSON schema required for validation inputs, or that will be matched by serialization outputs.

JsonSchemaWarningKind module-attribute

JsonSchemaWarningKind = Literal[
    "skipped-choice", "non-serializable-default"
]

A type alias representing the kinds of warnings that can be emitted during JSON schema generation.

See GenerateJsonSchema.render_warning_message for more details.

DEFAULT_REF_TEMPLATE module-attribute

DEFAULT_REF_TEMPLATE = '#/$defs/{model}'

The default format string used to generate reference names.

PydanticJsonSchemaWarning

Bases: UserWarning

This class is used to emit warnings produced during JSON schema generation. See the GenerateJsonSchema.emit_warning and GenerateJsonSchema.render_warning_message methods for more details; these can be overridden to control warning behavior.

GenerateJsonSchema

GenerateJsonSchema(
    by_alias=True, ref_template=DEFAULT_REF_TEMPLATE
)

A class for generating JSON schemas.

This class generates JSON schemas based on configured parameters. The default schema dialect is https://json-schema.org/draft/2020-12/schema. The class uses by_alias to configure how fields with multiple names are handled and ref_template to format reference names.

Attributes:

Name Type Description
schema_dialect

The JSON schema dialect used to generate the schema. See Declaring a Dialect in the JSON Schema documentation for more information about dialects.

ignored_warning_kinds set[JsonSchemaWarningKind]

Warnings to ignore when generating the schema. self.render_warning_message will do nothing if its argument kind is in ignored_warning_kinds; this value can be modified on subclasses to easily control which warnings are emitted.

by_alias

Whether or not to use field names when generating the schema.

ref_template

The format string used when generating reference names.

core_to_json_refs dict[CoreModeRef, JsonRef]

A mapping of core refs to JSON refs.

core_to_defs_refs dict[CoreModeRef, DefsRef]

A mapping of core refs to definition refs.

defs_to_core_refs dict[DefsRef, CoreModeRef]

A mapping of definition refs to core refs.

json_to_defs_refs dict[JsonRef, DefsRef]

A mapping of JSON refs to definition refs.

definitions dict[DefsRef, JsonSchemaValue]

Definitions in the schema.

collisions dict[DefsRef, JsonSchemaValue]

Definitions with colliding names. When collisions are detected, we choose a non-colliding name during generation, but we also track the colliding tag so that it can be remapped for the first occurrence at the end of the process.

defs_ref_fallbacks dict[DefsRef, JsonSchemaValue]

Core refs to fallback definitions refs.

_schema_type_to_method

A mapping of schema types to generator methods.

_used

Set to True after generating a schema to avoid re-use issues.

mode JsonSchemaMode

The schema mode.

Parameters:

Name Type Description Default
by_alias bool

Whether or not to include field names.

True
ref_template str

The format string to use when generating reference names.

DEFAULT_REF_TEMPLATE

Raises:

Type Description
JsonSchemaError

If the instance of the class is inadvertently re-used after generating a schema.

Source code in pydantic/json_schema.py
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
def __init__(self, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE):
    self.by_alias = by_alias
    self.ref_template = ref_template

    self.core_to_json_refs: dict[CoreModeRef, JsonRef] = {}
    self.core_to_defs_refs: dict[CoreModeRef, DefsRef] = {}
    self.defs_to_core_refs: dict[DefsRef, CoreModeRef] = {}
    self.json_to_defs_refs: dict[JsonRef, DefsRef] = {}

    self.definitions: dict[DefsRef, JsonSchemaValue] = {}

    self.mode: JsonSchemaMode = 'validation'

    # The following includes a mapping of a fully-unique defs ref choice to a list of preferred
    # alternatives, which are generally simpler, such as only including the class name.
    # At the end of schema generation, we use these to produce a JSON schema with more human-readable
    # definitions, which would also work better in a generated OpenAPI client, etc.
    self._prioritized_defsref_choices: dict[DefsRef, list[DefsRef]] = {}
    self._collision_counter: dict[str, int] = defaultdict(int)
    self._collision_index: dict[str, int] = {}

    self._schema_type_to_method = self.build_schema_type_to_method()

    # When we encounter definitions we need to try to build them immediately
    # so that they are available schemas that reference them
    # But it's possible that CoreSchema was never going to be used
    # (e.g. because the CoreSchema that references short circuits is JSON schema generation without needing
    #  the reference) so instead of failing altogether if we can't build a definition we
    # store the error raised and re-throw it if we end up needing that def
    self._core_defs_invalid_for_json_schema: dict[DefsRef, PydanticInvalidForJsonSchema] = {}

    # This changes to True after generating a schema, to prevent issues caused by accidental re-use
    # of a single instance of a schema generator
    self._used = False

ValidationsMapping

This class just contains mappings from core_schema attribute names to the corresponding JSON schema attribute names. While I suspect it is unlikely to be necessary, you can in principle override this class in a subclass of GenerateJsonSchema (by inheriting from GenerateJsonSchema.ValidationsMapping) to change these mappings.

build_schema_type_to_method

build_schema_type_to_method()

Builds a dictionary mapping fields to methods for generating JSON schemas.

Returns:

Type Description
dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]]

A dictionary containing the mapping of CoreSchemaOrFieldType to a handler method.

Raises:

Type Description
TypeError

If no method has been defined for generating a JSON schema for a given pydantic core schema type.

Source code in pydantic/json_schema.py
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
def build_schema_type_to_method(
    self,
) -> dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]]:
    """Builds a dictionary mapping fields to methods for generating JSON schemas.

    Returns:
        A dictionary containing the mapping of `CoreSchemaOrFieldType` to a handler method.

    Raises:
        TypeError: If no method has been defined for generating a JSON schema for a given pydantic core schema type.
    """
    mapping: dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]] = {}
    core_schema_types: list[CoreSchemaOrFieldType] = _typing_extra.all_literal_values(
        CoreSchemaOrFieldType  # type: ignore
    )
    for key in core_schema_types:
        method_name = f"{key.replace('-', '_')}_schema"
        try:
            mapping[key] = getattr(self, method_name)
        except AttributeError as e:  # pragma: no cover
            raise TypeError(
                f'No method for generating JsonSchema for core_schema.type={key!r} '
                f'(expected: {type(self).__name__}.{method_name})'
            ) from e
    return mapping

generate_definitions

generate_definitions(inputs)

Generates JSON schema definitions from a list of core schemas, pairing the generated definitions with a mapping that links the input keys to the definition references.

Parameters:

Name Type Description Default
inputs Sequence[tuple[JsonSchemaKeyT, JsonSchemaMode, core_schema.CoreSchema]]

A sequence of tuples, where:

  • The first element is a JSON schema key type.
  • The second element is the JSON mode: either 'validation' or 'serialization'.
  • The third element is a core schema.
required

Returns:

Type Description
tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], dict[DefsRef, JsonSchemaValue]]

A tuple where:

  • The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have JsonRef references to definitions that are defined in the second returned element.)
  • The second element is a dictionary whose keys are definition references for the JSON schemas from the first returned element, and whose values are the actual JSON schema definitions.

Raises:

Type Description
PydanticUserError

Raised if the JSON schema generator has already been used to generate a JSON schema.

Source code in pydantic/json_schema.py
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
def generate_definitions(
    self, inputs: Sequence[tuple[JsonSchemaKeyT, JsonSchemaMode, core_schema.CoreSchema]]
) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], dict[DefsRef, JsonSchemaValue]]:
    """Generates JSON schema definitions from a list of core schemas, pairing the generated definitions with a
    mapping that links the input keys to the definition references.

    Args:
        inputs: A sequence of tuples, where:

            - The first element is a JSON schema key type.
            - The second element is the JSON mode: either 'validation' or 'serialization'.
            - The third element is a core schema.

    Returns:
        A tuple where:

            - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
                whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
                JsonRef references to definitions that are defined in the second returned element.)
            - The second element is a dictionary whose keys are definition references for the JSON schemas
                from the first returned element, and whose values are the actual JSON schema definitions.

    Raises:
        PydanticUserError: Raised if the JSON schema generator has already been used to generate a JSON schema.
    """
    if self._used:
        raise PydanticUserError(
            'This JSON schema generator has already been used to generate a JSON schema. '
            f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.',
            code='json-schema-already-used',
        )

    for key, mode, schema in inputs:
        self.mode = mode
        self.generate_inner(schema)

    definitions_remapping = self._build_definitions_remapping()

    json_schemas_map: dict[tuple[JsonSchemaKeyT, JsonSchemaMode], DefsRef] = {}
    for key, mode, schema in inputs:
        self.mode = mode
        json_schema = self.generate_inner(schema)
        json_schemas_map[(key, mode)] = definitions_remapping.remap_json_schema(json_schema)

    json_schema = {'$defs': self.definitions}
    json_schema = definitions_remapping.remap_json_schema(json_schema)
    self._used = True
    return json_schemas_map, _sort_json_schema(json_schema['$defs'])  # type: ignore

generate

generate(schema, mode='validation')

Generates a JSON schema for a specified schema in a specified mode.

Parameters:

Name Type Description Default
schema CoreSchema

A Pydantic model.

required
mode JsonSchemaMode

The mode in which to generate the schema. Defaults to 'validation'.

'validation'

Returns:

Type Description
JsonSchemaValue

A JSON schema representing the specified schema.

Raises:

Type Description
PydanticUserError

If the JSON schema generator has already been used to generate a JSON schema.

Source code in pydantic/json_schema.py
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
def generate(self, schema: CoreSchema, mode: JsonSchemaMode = 'validation') -> JsonSchemaValue:
    """Generates a JSON schema for a specified schema in a specified mode.

    Args:
        schema: A Pydantic model.
        mode: The mode in which to generate the schema. Defaults to 'validation'.

    Returns:
        A JSON schema representing the specified schema.

    Raises:
        PydanticUserError: If the JSON schema generator has already been used to generate a JSON schema.
    """
    self.mode = mode
    if self._used:
        raise PydanticUserError(
            'This JSON schema generator has already been used to generate a JSON schema. '
            f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.',
            code='json-schema-already-used',
        )

    json_schema: JsonSchemaValue = self.generate_inner(schema)
    json_ref_counts = self.get_json_ref_counts(json_schema)

    # Remove the top-level $ref if present; note that the _generate method already ensures there are no sibling keys
    ref = cast(JsonRef, json_schema.get('$ref'))
    while ref is not None:  # may need to unpack multiple levels
        ref_json_schema = self.get_schema_from_definitions(ref)
        if json_ref_counts[ref] > 1 or ref_json_schema is None:
            # Keep the ref, but use an allOf to remove the top level $ref
            json_schema = {'allOf': [{'$ref': ref}]}
        else:
            # "Unpack" the ref since this is the only reference
            json_schema = ref_json_schema.copy()  # copy to prevent recursive dict reference
            json_ref_counts[ref] -= 1
        ref = cast(JsonRef, json_schema.get('$ref'))

    self._garbage_collect_definitions(json_schema)
    definitions_remapping = self._build_definitions_remapping()

    if self.definitions:
        json_schema['$defs'] = self.definitions

    json_schema = definitions_remapping.remap_json_schema(json_schema)

    # For now, we will not set the $schema key. However, if desired, this can be easily added by overriding
    # this method and adding the following line after a call to super().generate(schema):
    # json_schema['$schema'] = self.schema_dialect

    self._used = True
    return _sort_json_schema(json_schema)

generate_inner

generate_inner(schema)

Generates a JSON schema for a given core schema.

Parameters:

Name Type Description Default
schema CoreSchemaOrField

The given core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue:  # noqa: C901
    """Generates a JSON schema for a given core schema.

    Args:
        schema: The given core schema.

    Returns:
        The generated JSON schema.
    """
    # If a schema with the same CoreRef has been handled, just return a reference to it
    # Note that this assumes that it will _never_ be the case that the same CoreRef is used
    # on types that should have different JSON schemas
    if 'ref' in schema:
        core_ref = CoreRef(schema['ref'])  # type: ignore[typeddict-item]
        core_mode_ref = (core_ref, self.mode)
        if core_mode_ref in self.core_to_defs_refs and self.core_to_defs_refs[core_mode_ref] in self.definitions:
            return {'$ref': self.core_to_json_refs[core_mode_ref]}

    # Generate the JSON schema, accounting for the json_schema_override and core_schema_override
    metadata_handler = _core_metadata.CoreMetadataHandler(schema)

    def populate_defs(core_schema: CoreSchema, json_schema: JsonSchemaValue) -> JsonSchemaValue:
        if 'ref' in core_schema:
            core_ref = CoreRef(core_schema['ref'])  # type: ignore[typeddict-item]
            defs_ref, ref_json_schema = self.get_cache_defs_ref_schema(core_ref)
            json_ref = JsonRef(ref_json_schema['$ref'])
            self.json_to_defs_refs[json_ref] = defs_ref
            # Replace the schema if it's not a reference to itself
            # What we want to avoid is having the def be just a ref to itself
            # which is what would happen if we blindly assigned any
            if json_schema.get('$ref', None) != json_ref:
                self.definitions[defs_ref] = json_schema
                self._core_defs_invalid_for_json_schema.pop(defs_ref, None)
            json_schema = ref_json_schema
        return json_schema

    def convert_to_all_of(json_schema: JsonSchemaValue) -> JsonSchemaValue:
        if '$ref' in json_schema and len(json_schema.keys()) > 1:
            # technically you can't have any other keys next to a "$ref"
            # but it's an easy mistake to make and not hard to correct automatically here
            json_schema = json_schema.copy()
            ref = json_schema.pop('$ref')
            json_schema = {'allOf': [{'$ref': ref}], **json_schema}
        return json_schema

    def handler_func(schema_or_field: CoreSchemaOrField) -> JsonSchemaValue:
        """Generate a JSON schema based on the input schema.

        Args:
            schema_or_field: The core schema to generate a JSON schema from.

        Returns:
            The generated JSON schema.

        Raises:
            TypeError: If an unexpected schema type is encountered.
        """
        # Generate the core-schema-type-specific bits of the schema generation:
        json_schema: JsonSchemaValue | None = None
        if self.mode == 'serialization' and 'serialization' in schema_or_field:
            ser_schema = schema_or_field['serialization']  # type: ignore
            json_schema = self.ser_schema(ser_schema)
        if json_schema is None:
            if _core_utils.is_core_schema(schema_or_field) or _core_utils.is_core_schema_field(schema_or_field):
                generate_for_schema_type = self._schema_type_to_method[schema_or_field['type']]
                json_schema = generate_for_schema_type(schema_or_field)
            else:
                raise TypeError(f'Unexpected schema type: schema={schema_or_field}')
        if _core_utils.is_core_schema(schema_or_field):
            json_schema = populate_defs(schema_or_field, json_schema)
            json_schema = convert_to_all_of(json_schema)
        return json_schema

    current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, handler_func)

    for js_modify_function in metadata_handler.metadata.get('pydantic_js_functions', ()):

        def new_handler_func(
            schema_or_field: CoreSchemaOrField,
            current_handler: GetJsonSchemaHandler = current_handler,
            js_modify_function: GetJsonSchemaFunction = js_modify_function,
        ) -> JsonSchemaValue:
            json_schema = js_modify_function(schema_or_field, current_handler)
            if _core_utils.is_core_schema(schema_or_field):
                json_schema = populate_defs(schema_or_field, json_schema)
            original_schema = current_handler.resolve_ref_schema(json_schema)
            ref = json_schema.pop('$ref', None)
            if ref and json_schema:
                original_schema.update(json_schema)
            return original_schema

        current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func)

    for js_modify_function in metadata_handler.metadata.get('pydantic_js_annotation_functions', ()):

        def new_handler_func(
            schema_or_field: CoreSchemaOrField,
            current_handler: GetJsonSchemaHandler = current_handler,
            js_modify_function: GetJsonSchemaFunction = js_modify_function,
        ) -> JsonSchemaValue:
            json_schema = js_modify_function(schema_or_field, current_handler)
            if _core_utils.is_core_schema(schema_or_field):
                json_schema = populate_defs(schema_or_field, json_schema)
                json_schema = convert_to_all_of(json_schema)
            return json_schema

        current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func)

    json_schema = current_handler(schema)
    if _core_utils.is_core_schema(schema):
        json_schema = populate_defs(schema, json_schema)
        json_schema = convert_to_all_of(json_schema)
    return json_schema

any_schema

any_schema(schema)

Generates a JSON schema that matches any value.

Parameters:

Name Type Description Default
schema core_schema.AnySchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
536
537
538
539
540
541
542
543
544
545
def any_schema(self, schema: core_schema.AnySchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches any value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {}

none_schema

none_schema(schema)

Generates a JSON schema that matches a None value.

Parameters:

Name Type Description Default
schema core_schema.NoneSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
547
548
549
550
551
552
553
554
555
556
def none_schema(self, schema: core_schema.NoneSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a None value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'null'}

bool_schema

bool_schema(schema)

Generates a JSON schema that matches a bool value.

Parameters:

Name Type Description Default
schema core_schema.BoolSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
558
559
560
561
562
563
564
565
566
567
def bool_schema(self, schema: core_schema.BoolSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a bool value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'boolean'}

int_schema

int_schema(schema)

Generates a JSON schema that matches an Int value.

Parameters:

Name Type Description Default
schema core_schema.IntSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
569
570
571
572
573
574
575
576
577
578
579
580
581
def int_schema(self, schema: core_schema.IntSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches an Int value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema: dict[str, Any] = {'type': 'integer'}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric)
    json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}}
    return json_schema

float_schema

float_schema(schema)

Generates a JSON schema that matches a float value.

Parameters:

Name Type Description Default
schema core_schema.FloatSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
583
584
585
586
587
588
589
590
591
592
593
594
595
def float_schema(self, schema: core_schema.FloatSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a float value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema: dict[str, Any] = {'type': 'number'}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric)
    json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}}
    return json_schema

decimal_schema

decimal_schema(schema)

Generates a JSON schema that matches a decimal value.

Parameters:

Name Type Description Default
schema core_schema.DecimalSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
def decimal_schema(self, schema: core_schema.DecimalSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a decimal value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = self.str_schema(core_schema.str_schema())
    if self.mode == 'validation':
        multiple_of = schema.get('multiple_of')
        le = schema.get('le')
        ge = schema.get('ge')
        lt = schema.get('lt')
        gt = schema.get('gt')
        json_schema = {
            'anyOf': [
                self.float_schema(
                    core_schema.float_schema(
                        allow_inf_nan=schema.get('allow_inf_nan'),
                        multiple_of=None if multiple_of is None else float(multiple_of),
                        le=None if le is None else float(le),
                        ge=None if ge is None else float(ge),
                        lt=None if lt is None else float(lt),
                        gt=None if gt is None else float(gt),
                    )
                ),
                json_schema,
            ],
        }
    return json_schema

str_schema

str_schema(schema)

Generates a JSON schema that matches a string value.

Parameters:

Name Type Description Default
schema core_schema.StringSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
630
631
632
633
634
635
636
637
638
639
640
641
def str_schema(self, schema: core_schema.StringSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a string value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = {'type': 'string'}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
    return json_schema

bytes_schema

bytes_schema(schema)

Generates a JSON schema that matches a bytes value.

Parameters:

Name Type Description Default
schema core_schema.BytesSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
643
644
645
646
647
648
649
650
651
652
653
654
def bytes_schema(self, schema: core_schema.BytesSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a bytes value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = {'type': 'string', 'format': 'binary'}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes)
    return json_schema

date_schema

date_schema(schema)

Generates a JSON schema that matches a date value.

Parameters:

Name Type Description Default
schema core_schema.DateSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
656
657
658
659
660
661
662
663
664
665
666
667
def date_schema(self, schema: core_schema.DateSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a date value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = {'type': 'string', 'format': 'date'}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.date)
    return json_schema

time_schema

time_schema(schema)

Generates a JSON schema that matches a time value.

Parameters:

Name Type Description Default
schema core_schema.TimeSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
669
670
671
672
673
674
675
676
677
678
def time_schema(self, schema: core_schema.TimeSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a time value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'string', 'format': 'time'}

datetime_schema

datetime_schema(schema)

Generates a JSON schema that matches a datetime value.

Parameters:

Name Type Description Default
schema core_schema.DatetimeSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
680
681
682
683
684
685
686
687
688
689
def datetime_schema(self, schema: core_schema.DatetimeSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a datetime value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'string', 'format': 'date-time'}

timedelta_schema

timedelta_schema(schema)

Generates a JSON schema that matches a timedelta value.

Parameters:

Name Type Description Default
schema core_schema.TimedeltaSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
691
692
693
694
695
696
697
698
699
700
def timedelta_schema(self, schema: core_schema.TimedeltaSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a timedelta value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'string', 'format': 'duration'}

literal_schema

literal_schema(schema)

Generates a JSON schema that matches a literal value.

Parameters:

Name Type Description Default
schema core_schema.LiteralSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
def literal_schema(self, schema: core_schema.LiteralSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a literal value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    expected = [v.value if isinstance(v, Enum) else v for v in schema['expected']]
    # jsonify the expected values
    expected = [to_jsonable_python(v) for v in expected]

    if len(expected) == 1:
        return {'const': expected[0]}

    types = {type(e) for e in expected}
    if types == {str}:
        return {'enum': expected, 'type': 'string'}
    elif types == {int}:
        return {'enum': expected, 'type': 'integer'}
    elif types == {float}:
        return {'enum': expected, 'type': 'number'}
    elif types == {bool}:
        return {'enum': expected, 'type': 'boolean'}
    elif types == {list}:
        return {'enum': expected, 'type': 'array'}
    # there is not None case because if it's mixed it hits the final `else`
    # if it's a single Literal[None] then it becomes a `const` schema above
    else:
        return {'enum': expected}

is_instance_schema

is_instance_schema(schema)

Generates a JSON schema that checks if a value is an instance of a class, equivalent to Python's isinstance method.

Parameters:

Name Type Description Default
schema core_schema.IsInstanceSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
734
735
736
737
738
739
740
741
742
743
744
def is_instance_schema(self, schema: core_schema.IsInstanceSchema) -> JsonSchemaValue:
    """Generates a JSON schema that checks if a value is an instance of a class, equivalent to Python's
    `isinstance` method.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.handle_invalid_for_json_schema(schema, f'core_schema.IsInstanceSchema ({schema["cls"]})')

is_subclass_schema

is_subclass_schema(schema)

Generates a JSON schema that checks if a value is a subclass of a class, equivalent to Python's issubclass method.

Parameters:

Name Type Description Default
schema core_schema.IsSubclassSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
746
747
748
749
750
751
752
753
754
755
756
757
def is_subclass_schema(self, schema: core_schema.IsSubclassSchema) -> JsonSchemaValue:
    """Generates a JSON schema that checks if a value is a subclass of a class, equivalent to Python's `issubclass`
    method.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    # Note: This is for compatibility with V1; you can override if you want different behavior.
    return {}

callable_schema

callable_schema(schema)

Generates a JSON schema that matches a callable value.

Parameters:

Name Type Description Default
schema core_schema.CallableSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
759
760
761
762
763
764
765
766
767
768
def callable_schema(self, schema: core_schema.CallableSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a callable value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.handle_invalid_for_json_schema(schema, 'core_schema.CallableSchema')

list_schema

list_schema(schema)

Returns a schema that matches a list schema.

Parameters:

Name Type Description Default
schema core_schema.ListSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
770
771
772
773
774
775
776
777
778
779
780
781
782
def list_schema(self, schema: core_schema.ListSchema) -> JsonSchemaValue:
    """Returns a schema that matches a list schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
    json_schema = {'type': 'array', 'items': items_schema}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
    return json_schema

tuple_positional_schema

tuple_positional_schema(schema)

Generates a JSON schema that matches a positional tuple schema e.g. Tuple[int, str, bool].

Parameters:

Name Type Description Default
schema core_schema.TuplePositionalSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
def tuple_positional_schema(self, schema: core_schema.TuplePositionalSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a positional tuple schema e.g. `Tuple[int, str, bool]`.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema: JsonSchemaValue = {'type': 'array'}
    json_schema['minItems'] = len(schema['items_schema'])
    prefixItems = [self.generate_inner(item) for item in schema['items_schema']]
    if prefixItems:
        json_schema['prefixItems'] = prefixItems
    if 'extra_schema' in schema:
        json_schema['items'] = self.generate_inner(schema['extra_schema'])
    else:
        json_schema['maxItems'] = len(schema['items_schema'])
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
    return json_schema

tuple_variable_schema

tuple_variable_schema(schema)

Generates a JSON schema that matches a variable tuple schema e.g. Tuple[int, ...].

Parameters:

Name Type Description Default
schema core_schema.TupleVariableSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
805
806
807
808
809
810
811
812
813
814
815
816
817
def tuple_variable_schema(self, schema: core_schema.TupleVariableSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a variable tuple schema e.g. `Tuple[int, ...]`.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
    json_schema = {'type': 'array', 'items': items_schema}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
    return json_schema

set_schema

set_schema(schema)

Generates a JSON schema that matches a set schema.

Parameters:

Name Type Description Default
schema core_schema.SetSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
819
820
821
822
823
824
825
826
827
828
def set_schema(self, schema: core_schema.SetSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a set schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._common_set_schema(schema)

frozenset_schema

frozenset_schema(schema)

Generates a JSON schema that matches a frozenset schema.

Parameters:

Name Type Description Default
schema core_schema.FrozenSetSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
830
831
832
833
834
835
836
837
838
839
def frozenset_schema(self, schema: core_schema.FrozenSetSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a frozenset schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._common_set_schema(schema)

generator_schema

generator_schema(schema)

Returns a JSON schema that represents the provided GeneratorSchema.

Parameters:

Name Type Description Default
schema core_schema.GeneratorSchema

The schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
847
848
849
850
851
852
853
854
855
856
857
858
859
def generator_schema(self, schema: core_schema.GeneratorSchema) -> JsonSchemaValue:
    """Returns a JSON schema that represents the provided GeneratorSchema.

    Args:
        schema: The schema.

    Returns:
        The generated JSON schema.
    """
    items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
    json_schema = {'type': 'array', 'items': items_schema}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
    return json_schema

dict_schema

dict_schema(schema)

Generates a JSON schema that matches a dict schema.

Parameters:

Name Type Description Default
schema core_schema.DictSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
def dict_schema(self, schema: core_schema.DictSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a dict schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema: JsonSchemaValue = {'type': 'object'}

    keys_schema = self.generate_inner(schema['keys_schema']).copy() if 'keys_schema' in schema else {}
    keys_pattern = keys_schema.pop('pattern', None)

    values_schema = self.generate_inner(schema['values_schema']).copy() if 'values_schema' in schema else {}
    values_schema.pop('title', None)  # don't give a title to the additionalProperties
    if values_schema or keys_pattern is not None:  # don't add additionalProperties if it's empty
        if keys_pattern is None:
            json_schema['additionalProperties'] = values_schema
        else:
            json_schema['patternProperties'] = {keys_pattern: values_schema}

    self.update_with_validations(json_schema, schema, self.ValidationsMapping.object)
    return json_schema

function_before_schema

function_before_schema(schema)

Generates a JSON schema that matches a function-before schema.

Parameters:

Name Type Description Default
schema core_schema.BeforeValidatorFunctionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
900
901
902
903
904
905
906
907
908
909
def function_before_schema(self, schema: core_schema.BeforeValidatorFunctionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a function-before schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._function_schema(schema)

function_after_schema

function_after_schema(schema)

Generates a JSON schema that matches a function-after schema.

Parameters:

Name Type Description Default
schema core_schema.AfterValidatorFunctionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
911
912
913
914
915
916
917
918
919
920
def function_after_schema(self, schema: core_schema.AfterValidatorFunctionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a function-after schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._function_schema(schema)

function_plain_schema

function_plain_schema(schema)

Generates a JSON schema that matches a function-plain schema.

Parameters:

Name Type Description Default
schema core_schema.PlainValidatorFunctionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
922
923
924
925
926
927
928
929
930
931
def function_plain_schema(self, schema: core_schema.PlainValidatorFunctionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a function-plain schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._function_schema(schema)

function_wrap_schema

function_wrap_schema(schema)

Generates a JSON schema that matches a function-wrap schema.

Parameters:

Name Type Description Default
schema core_schema.WrapValidatorFunctionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
933
934
935
936
937
938
939
940
941
942
def function_wrap_schema(self, schema: core_schema.WrapValidatorFunctionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a function-wrap schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self._function_schema(schema)

default_schema

default_schema(schema)

Generates a JSON schema that matches a schema with a default value.

Parameters:

Name Type Description Default
schema core_schema.WithDefaultSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
def default_schema(self, schema: core_schema.WithDefaultSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema with a default value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = self.generate_inner(schema['schema'])

    if 'default' not in schema:
        return json_schema
    default = schema['default']
    # Note: if you want to include the value returned by the default_factory,
    # override this method and replace the code above with:
    # if 'default' in schema:
    #     default = schema['default']
    # elif 'default_factory' in schema:
    #     default = schema['default_factory']()
    # else:
    #     return json_schema

    try:
        encoded_default = self.encode_default(default)
    except pydantic_core.PydanticSerializationError:
        self.emit_warning(
            'non-serializable-default',
            f'Default value {default} is not JSON serializable; excluding default from JSON schema',
        )
        # Return the inner schema, as though there was no default
        return json_schema

    if '$ref' in json_schema:
        # Since reference schemas do not support child keys, we wrap the reference schema in a single-case allOf:
        return {'allOf': [json_schema], 'default': encoded_default}
    else:
        json_schema['default'] = encoded_default
        return json_schema

nullable_schema

nullable_schema(schema)

Generates a JSON schema that matches a schema that allows null values.

Parameters:

Name Type Description Default
schema core_schema.NullableSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
def nullable_schema(self, schema: core_schema.NullableSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that allows null values.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    null_schema = {'type': 'null'}
    inner_json_schema = self.generate_inner(schema['schema'])

    if inner_json_schema == null_schema:
        return null_schema
    else:
        # Thanks to the equality check against `null_schema` above, I think 'oneOf' would also be valid here;
        # I'll use 'anyOf' for now, but it could be changed it if it would work better with some external tooling
        return self.get_flattened_anyof([inner_json_schema, null_schema])

union_schema

union_schema(schema)

Generates a JSON schema that matches a schema that allows values matching any of the given schemas.

Parameters:

Name Type Description Default
schema core_schema.UnionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
def union_schema(self, schema: core_schema.UnionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that allows values matching any of the given schemas.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    generated: list[JsonSchemaValue] = []

    choices = schema['choices']
    for choice in choices:
        # choice will be a tuple if an explicit label was provided
        choice_schema = choice[0] if isinstance(choice, tuple) else choice
        try:
            generated.append(self.generate_inner(choice_schema))
        except PydanticOmit:
            continue
        except PydanticInvalidForJsonSchema as exc:
            self.emit_warning('skipped-choice', exc.message)
    if len(generated) == 1:
        return generated[0]
    return self.get_flattened_anyof(generated)

tagged_union_schema

tagged_union_schema(schema)

Generates a JSON schema that matches a schema that allows values matching any of the given schemas, where the schemas are tagged with a discriminator field that indicates which schema should be used to validate the value.

Parameters:

Name Type Description Default
schema core_schema.TaggedUnionSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
def tagged_union_schema(self, schema: core_schema.TaggedUnionSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that allows values matching any of the given schemas, where
    the schemas are tagged with a discriminator field that indicates which schema should be used to validate
    the value.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    generated: dict[str, JsonSchemaValue] = {}
    for k, v in schema['choices'].items():
        if isinstance(k, Enum):
            k = k.value
        try:
            # Use str(k) since keys must be strings for json; while not technically correct,
            # it's the closest that can be represented in valid JSON
            generated[str(k)] = self.generate_inner(v).copy()
        except PydanticOmit:
            continue
        except PydanticInvalidForJsonSchema as exc:
            self.emit_warning('skipped-choice', exc.message)

    one_of_choices = _deduplicate_schemas(generated.values())
    json_schema: JsonSchemaValue = {'oneOf': one_of_choices}

    # This reflects the v1 behavior; TODO: we should make it possible to exclude OpenAPI stuff from the JSON schema
    openapi_discriminator = self._extract_discriminator(schema, one_of_choices)
    if openapi_discriminator is not None:
        json_schema['discriminator'] = {
            'propertyName': openapi_discriminator,
            'mapping': {k: v.get('$ref', v) for k, v in generated.items()},
        }

    return json_schema

chain_schema

chain_schema(schema)

Generates a JSON schema that matches a core_schema.ChainSchema.

When generating a schema for validation, we return the validation JSON schema for the first step in the chain. For serialization, we return the serialization JSON schema for the last step in the chain.

Parameters:

Name Type Description Default
schema core_schema.ChainSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
def chain_schema(self, schema: core_schema.ChainSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a core_schema.ChainSchema.

    When generating a schema for validation, we return the validation JSON schema for the first step in the chain.
    For serialization, we return the serialization JSON schema for the last step in the chain.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    step_index = 0 if self.mode == 'validation' else -1  # use first step for validation, last for serialization
    return self.generate_inner(schema['steps'][step_index])

lax_or_strict_schema

lax_or_strict_schema(schema)

Generates a JSON schema that matches a schema that allows values matching either the lax schema or the strict schema.

Parameters:

Name Type Description Default
schema core_schema.LaxOrStrictSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
def lax_or_strict_schema(self, schema: core_schema.LaxOrStrictSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that allows values matching either the lax schema or the
    strict schema.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    # TODO: Need to read the default value off of model config or whatever
    use_strict = schema.get('strict', False)  # TODO: replace this default False
    # If your JSON schema fails to generate it is probably
    # because one of the following two branches failed.
    if use_strict:
        return self.generate_inner(schema['strict_schema'])
    else:
        return self.generate_inner(schema['lax_schema'])

json_or_python_schema

json_or_python_schema(schema)

Generates a JSON schema that matches a schema that allows values matching either the JSON schema or the Python schema.

The JSON schema is used instead of the Python schema. If you want to use the Python schema, you should override this method.

Parameters:

Name Type Description Default
schema core_schema.JsonOrPythonSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
def json_or_python_schema(self, schema: core_schema.JsonOrPythonSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that allows values matching either the JSON schema or the
    Python schema.

    The JSON schema is used instead of the Python schema. If you want to use the Python schema, you should override
    this method.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['json_schema'])

typed_dict_schema

typed_dict_schema(schema)

Generates a JSON schema that matches a schema that defines a typed dict.

Parameters:

Name Type Description Default
schema core_schema.TypedDictSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
def typed_dict_schema(self, schema: core_schema.TypedDictSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a typed dict.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    total = schema.get('total', True)
    named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
        (name, self.field_is_required(field, total), field)
        for name, field in schema['fields'].items()
        if self.field_is_present(field)
    ]
    if self.mode == 'serialization':
        named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
    json_schema = self._named_required_fields_schema(named_required_fields)
    config: CoreConfig | None = schema.get('config', None)

    extra = (config or {}).get('extra_fields_behavior', 'ignore')
    if extra == 'forbid':
        json_schema['additionalProperties'] = False
    elif extra == 'allow':
        json_schema['additionalProperties'] = True

    return json_schema

typed_dict_field_schema

typed_dict_field_schema(schema)

Generates a JSON schema that matches a schema that defines a typed dict field.

Parameters:

Name Type Description Default
schema core_schema.TypedDictField

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
def typed_dict_field_schema(self, schema: core_schema.TypedDictField) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a typed dict field.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['schema'])

dataclass_field_schema

dataclass_field_schema(schema)

Generates a JSON schema that matches a schema that defines a dataclass field.

Parameters:

Name Type Description Default
schema core_schema.DataclassField

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
def dataclass_field_schema(self, schema: core_schema.DataclassField) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a dataclass field.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['schema'])

model_field_schema

model_field_schema(schema)

Generates a JSON schema that matches a schema that defines a model field.

Parameters:

Name Type Description Default
schema core_schema.ModelField

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
def model_field_schema(self, schema: core_schema.ModelField) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a model field.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['schema'])

computed_field_schema

computed_field_schema(schema)

Generates a JSON schema that matches a schema that defines a computed field.

Parameters:

Name Type Description Default
schema core_schema.ComputedField

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
def computed_field_schema(self, schema: core_schema.ComputedField) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a computed field.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['return_schema'])

model_schema

model_schema(schema)

Generates a JSON schema that matches a schema that defines a model.

Parameters:

Name Type Description Default
schema core_schema.ModelSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a model.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    # We do not use schema['model'].model_json_schema() here
    # because it could lead to inconsistent refs handling, etc.
    json_schema = self.generate_inner(schema['schema'])

    cls = cast('type[BaseModel]', schema['cls'])
    config = cls.model_config
    title = config.get('title')

    json_schema_extra = config.get('json_schema_extra')
    if cls.__pydantic_root_model__:
        root_json_schema_extra = cls.model_fields['root'].json_schema_extra
        if json_schema_extra and root_json_schema_extra:
            raise ValueError(
                '"model_config[\'json_schema_extra\']" and "Field.json_schema_extra" on "RootModel.root"'
                ' field must not be set simultaneously'
            )
        if root_json_schema_extra:
            json_schema_extra = root_json_schema_extra

    json_schema = self._update_class_schema(json_schema, title, config.get('extra', None), cls, json_schema_extra)

    return json_schema

resolve_schema_to_update

resolve_schema_to_update(json_schema)

Resolve a JsonSchemaValue to the non-ref schema if it is a $ref schema.

Parameters:

Name Type Description Default
json_schema JsonSchemaValue

The schema to resolve.

required

Returns:

Type Description
JsonSchemaValue

The resolved schema.

Source code in pydantic/json_schema.py
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
def resolve_schema_to_update(self, json_schema: JsonSchemaValue) -> JsonSchemaValue:
    """Resolve a JsonSchemaValue to the non-ref schema if it is a $ref schema.

    Args:
        json_schema: The schema to resolve.

    Returns:
        The resolved schema.
    """
    if '$ref' in json_schema:
        schema_to_update = self.get_schema_from_definitions(JsonRef(json_schema['$ref']))
        if schema_to_update is None:
            raise RuntimeError(f'Cannot update undefined schema for $ref={json_schema["$ref"]}')
        return self.resolve_schema_to_update(schema_to_update)
    else:
        schema_to_update = json_schema
    return schema_to_update

model_fields_schema

model_fields_schema(schema)

Generates a JSON schema that matches a schema that defines a model's fields.

Parameters:

Name Type Description Default
schema core_schema.ModelFieldsSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
def model_fields_schema(self, schema: core_schema.ModelFieldsSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a model's fields.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
        (name, self.field_is_required(field, total=True), field)
        for name, field in schema['fields'].items()
        if self.field_is_present(field)
    ]
    if self.mode == 'serialization':
        named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
    json_schema = self._named_required_fields_schema(named_required_fields)
    extra_validator = schema.get('extra_validator', None)
    if extra_validator is not None:
        schema_to_update = self.resolve_schema_to_update(json_schema)
        schema_to_update['additionalProperties'] = self.generate_inner(extra_validator)
    return json_schema

field_is_present

field_is_present(field)

Whether the field should be included in the generated JSON schema.

Parameters:

Name Type Description Default
field CoreSchemaField

The schema for the field itself.

required

Returns:

Type Description
bool

True if the field should be included in the generated JSON schema, False otherwise.

Source code in pydantic/json_schema.py
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
def field_is_present(self, field: CoreSchemaField) -> bool:
    """Whether the field should be included in the generated JSON schema.

    Args:
        field: The schema for the field itself.

    Returns:
        `True` if the field should be included in the generated JSON schema, `False` otherwise.
    """
    if self.mode == 'serialization':
        # If you still want to include the field in the generated JSON schema,
        # override this method and return True
        return not field.get('serialization_exclude')
    elif self.mode == 'validation':
        return True
    else:
        assert_never(self.mode)

field_is_required

field_is_required(field, total)

Whether the field should be marked as required in the generated JSON schema. (Note that this is irrelevant if the field is not present in the JSON schema.).

Parameters:

Name Type Description Default
field core_schema.ModelField | core_schema.DataclassField | core_schema.TypedDictField

The schema for the field itself.

required
total bool

Only applies to TypedDictFields. Indicates if the TypedDict this field belongs to is total, in which case any fields that don't explicitly specify required=False are required.

required

Returns:

Type Description
bool

True if the field should be marked as required in the generated JSON schema, False otherwise.

Source code in pydantic/json_schema.py
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
def field_is_required(
    self,
    field: core_schema.ModelField | core_schema.DataclassField | core_schema.TypedDictField,
    total: bool,
) -> bool:
    """Whether the field should be marked as required in the generated JSON schema.
    (Note that this is irrelevant if the field is not present in the JSON schema.).

    Args:
        field: The schema for the field itself.
        total: Only applies to `TypedDictField`s.
            Indicates if the `TypedDict` this field belongs to is total, in which case any fields that don't
            explicitly specify `required=False` are required.

    Returns:
        `True` if the field should be marked as required in the generated JSON schema, `False` otherwise.
    """
    if self.mode == 'serialization':
        return not field.get('serialization_exclude')
    elif self.mode == 'validation':
        if field['type'] == 'typed-dict-field':
            return field.get('required', total)
        else:
            return field['schema']['type'] != 'default'
    else:
        assert_never(self.mode)

dataclass_args_schema

dataclass_args_schema(schema)

Generates a JSON schema that matches a schema that defines a dataclass's constructor arguments.

Parameters:

Name Type Description Default
schema core_schema.DataclassArgsSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
def dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a dataclass's constructor arguments.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
        (field['name'], self.field_is_required(field, total=True), field)
        for field in schema['fields']
        if self.field_is_present(field)
    ]
    if self.mode == 'serialization':
        named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
    return self._named_required_fields_schema(named_required_fields)

dataclass_schema

dataclass_schema(schema)

Generates a JSON schema that matches a schema that defines a dataclass.

Parameters:

Name Type Description Default
schema core_schema.DataclassSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
def dataclass_schema(self, schema: core_schema.DataclassSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a dataclass.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = self.generate_inner(schema['schema']).copy()

    cls = schema['cls']
    config: ConfigDict = getattr(cls, '__pydantic_config__', cast('ConfigDict', {}))

    title = config.get('title') or cls.__name__

    json_schema_extra = config.get('json_schema_extra')
    json_schema = self._update_class_schema(json_schema, title, config.get('extra', None), cls, json_schema_extra)

    # Dataclass-specific handling of description
    if is_dataclass(cls) and not hasattr(cls, '__pydantic_validator__'):
        # vanilla dataclass; don't use cls.__doc__ as it will contain the class signature by default
        description = None
    else:
        description = None if cls.__doc__ is None else inspect.cleandoc(cls.__doc__)
    if description:
        json_schema['description'] = description

    return json_schema

arguments_schema

arguments_schema(schema)

Generates a JSON schema that matches a schema that defines a function's arguments.

Parameters:

Name Type Description Default
schema core_schema.ArgumentsSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
def arguments_schema(self, schema: core_schema.ArgumentsSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a function's arguments.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    metadata = _core_metadata.CoreMetadataHandler(schema).metadata
    prefer_positional = metadata.get('pydantic_js_prefer_positional_arguments')

    arguments = schema['arguments_schema']
    kw_only_arguments = [a for a in arguments if a.get('mode') == 'keyword_only']
    kw_or_p_arguments = [a for a in arguments if a.get('mode') in {'positional_or_keyword', None}]
    p_only_arguments = [a for a in arguments if a.get('mode') == 'positional_only']
    var_args_schema = schema.get('var_args_schema')
    var_kwargs_schema = schema.get('var_kwargs_schema')

    if prefer_positional:
        positional_possible = not kw_only_arguments and not var_kwargs_schema
        if positional_possible:
            return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema)

    keyword_possible = not p_only_arguments and not var_args_schema
    if keyword_possible:
        return self.kw_arguments_schema(kw_or_p_arguments + kw_only_arguments, var_kwargs_schema)

    if not prefer_positional:
        positional_possible = not kw_only_arguments and not var_kwargs_schema
        if positional_possible:
            return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema)

    # TODO: When support for Python 3.7 is dropped, uncomment the block on `test_json_schema`
    # to cover this test case.
    raise PydanticInvalidForJsonSchema(  # pragma: no cover
        'Unable to generate JSON schema for arguments validator with positional-only and keyword-only arguments'
    )

kw_arguments_schema

kw_arguments_schema(arguments, var_kwargs_schema)

Generates a JSON schema that matches a schema that defines a function's keyword arguments.

Parameters:

Name Type Description Default
arguments list[core_schema.ArgumentsParameter]

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
def kw_arguments_schema(
    self, arguments: list[core_schema.ArgumentsParameter], var_kwargs_schema: CoreSchema | None
) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a function's keyword arguments.

    Args:
        arguments: The core schema.

    Returns:
        The generated JSON schema.
    """
    properties: dict[str, JsonSchemaValue] = {}
    required: list[str] = []
    for argument in arguments:
        name = self.get_argument_name(argument)
        argument_schema = self.generate_inner(argument['schema']).copy()
        argument_schema['title'] = self.get_title_from_name(name)
        properties[name] = argument_schema

        if argument['schema']['type'] != 'default':
            # This assumes that if the argument has a default value,
            # the inner schema must be of type WithDefaultSchema.
            # I believe this is true, but I am not 100% sure
            required.append(name)

    json_schema: JsonSchemaValue = {'type': 'object', 'properties': properties}
    if required:
        json_schema['required'] = required

    if var_kwargs_schema:
        additional_properties_schema = self.generate_inner(var_kwargs_schema)
        if additional_properties_schema:
            json_schema['additionalProperties'] = additional_properties_schema
    else:
        json_schema['additionalProperties'] = False
    return json_schema

p_arguments_schema

p_arguments_schema(arguments, var_args_schema)

Generates a JSON schema that matches a schema that defines a function's positional arguments.

Parameters:

Name Type Description Default
arguments list[core_schema.ArgumentsParameter]

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
def p_arguments_schema(
    self, arguments: list[core_schema.ArgumentsParameter], var_args_schema: CoreSchema | None
) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a function's positional arguments.

    Args:
        arguments: The core schema.

    Returns:
        The generated JSON schema.
    """
    prefix_items: list[JsonSchemaValue] = []
    min_items = 0

    for argument in arguments:
        name = self.get_argument_name(argument)

        argument_schema = self.generate_inner(argument['schema']).copy()
        argument_schema['title'] = self.get_title_from_name(name)
        prefix_items.append(argument_schema)

        if argument['schema']['type'] != 'default':
            # This assumes that if the argument has a default value,
            # the inner schema must be of type WithDefaultSchema.
            # I believe this is true, but I am not 100% sure
            min_items += 1

    json_schema: JsonSchemaValue = {'type': 'array', 'prefixItems': prefix_items}
    if min_items:
        json_schema['minItems'] = min_items

    if var_args_schema:
        items_schema = self.generate_inner(var_args_schema)
        if items_schema:
            json_schema['items'] = items_schema
    else:
        json_schema['maxItems'] = len(prefix_items)

    return json_schema

get_argument_name

get_argument_name(argument)

Retrieves the name of an argument.

Parameters:

Name Type Description Default
argument core_schema.ArgumentsParameter

The core schema.

required

Returns:

Type Description
str

The name of the argument.

Source code in pydantic/json_schema.py
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
def get_argument_name(self, argument: core_schema.ArgumentsParameter) -> str:
    """Retrieves the name of an argument.

    Args:
        argument: The core schema.

    Returns:
        The name of the argument.
    """
    name = argument['name']
    if self.by_alias:
        alias = argument.get('alias')
        if isinstance(alias, str):
            name = alias
        else:
            pass  # might want to do something else?
    return name

call_schema

call_schema(schema)

Generates a JSON schema that matches a schema that defines a function call.

Parameters:

Name Type Description Default
schema core_schema.CallSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
def call_schema(self, schema: core_schema.CallSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a function call.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['arguments_schema'])

custom_error_schema

custom_error_schema(schema)

Generates a JSON schema that matches a schema that defines a custom error.

Parameters:

Name Type Description Default
schema core_schema.CustomErrorSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
def custom_error_schema(self, schema: core_schema.CustomErrorSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a custom error.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return self.generate_inner(schema['schema'])

json_schema

json_schema(schema)

Generates a JSON schema that matches a schema that defines a JSON object.

Parameters:

Name Type Description Default
schema core_schema.JsonSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
def json_schema(self, schema: core_schema.JsonSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a JSON object.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    content_core_schema = schema.get('schema') or core_schema.any_schema()
    content_json_schema = self.generate_inner(content_core_schema)
    if self.mode == 'validation':
        return {'type': 'string', 'contentMediaType': 'application/json', 'contentSchema': content_json_schema}
    else:
        # self.mode == 'serialization'
        return content_json_schema

url_schema

url_schema(schema)

Generates a JSON schema that matches a schema that defines a URL.

Parameters:

Name Type Description Default
schema core_schema.UrlSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
def url_schema(self, schema: core_schema.UrlSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a URL.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    json_schema = {'type': 'string', 'format': 'uri', 'minLength': 1}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
    return json_schema

multi_host_url_schema

multi_host_url_schema(schema)

Generates a JSON schema that matches a schema that defines a URL that can be used with multiple hosts.

Parameters:

Name Type Description Default
schema core_schema.MultiHostUrlSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
def multi_host_url_schema(self, schema: core_schema.MultiHostUrlSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a URL that can be used with multiple hosts.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    # Note: 'multi-host-uri' is a custom/pydantic-specific format, not part of the JSON Schema spec
    json_schema = {'type': 'string', 'format': 'multi-host-uri', 'minLength': 1}
    self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
    return json_schema

uuid_schema

uuid_schema(schema)

Generates a JSON schema that matches a UUID.

Parameters:

Name Type Description Default
schema core_schema.UuidSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
def uuid_schema(self, schema: core_schema.UuidSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a UUID.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    return {'type': 'string', 'format': 'uuid'}

definitions_schema

definitions_schema(schema)

Generates a JSON schema that matches a schema that defines a JSON object with definitions.

Parameters:

Name Type Description Default
schema core_schema.DefinitionsSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
def definitions_schema(self, schema: core_schema.DefinitionsSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that defines a JSON object with definitions.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    for definition in schema['definitions']:
        try:
            self.generate_inner(definition)
        except PydanticInvalidForJsonSchema as e:
            core_ref: CoreRef = CoreRef(definition['ref'])  # type: ignore
            self._core_defs_invalid_for_json_schema[self.get_defs_ref((core_ref, self.mode))] = e
            continue
    return self.generate_inner(schema['schema'])

definition_ref_schema

definition_ref_schema(schema)

Generates a JSON schema that matches a schema that references a definition.

Parameters:

Name Type Description Default
schema core_schema.DefinitionReferenceSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue

The generated JSON schema.

Source code in pydantic/json_schema.py
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
def definition_ref_schema(self, schema: core_schema.DefinitionReferenceSchema) -> JsonSchemaValue:
    """Generates a JSON schema that matches a schema that references a definition.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    core_ref = CoreRef(schema['schema_ref'])
    _, ref_json_schema = self.get_cache_defs_ref_schema(core_ref)
    return ref_json_schema

ser_schema

ser_schema(schema)

Generates a JSON schema that matches a schema that defines a serialized object.

Parameters:

Name Type Description Default
schema core_schema.SerSchema | core_schema.IncExSeqSerSchema | core_schema.IncExDictSerSchema

The core schema.

required

Returns:

Type Description
JsonSchemaValue | None

The generated JSON schema.

Source code in pydantic/json_schema.py
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
def ser_schema(
    self, schema: core_schema.SerSchema | core_schema.IncExSeqSerSchema | core_schema.IncExDictSerSchema
) -> JsonSchemaValue | None:
    """Generates a JSON schema that matches a schema that defines a serialized object.

    Args:
        schema: The core schema.

    Returns:
        The generated JSON schema.
    """
    schema_type = schema['type']
    if schema_type == 'function-plain' or schema_type == 'function-wrap':
        # PlainSerializerFunctionSerSchema or WrapSerializerFunctionSerSchema
        return_schema = schema.get('return_schema')
        if return_schema is not None:
            return self.generate_inner(return_schema)
    elif schema_type == 'format' or schema_type == 'to-string':
        # FormatSerSchema or ToStringSerSchema
        return self.str_schema(core_schema.str_schema())
    elif schema['type'] == 'model':
        # ModelSerSchema
        return self.generate_inner(schema['schema'])
    return None

get_title_from_name

get_title_from_name(name)

Retrieves a title from a name.

Parameters:

Name Type Description Default
name str

The name to retrieve a title from.

required

Returns:

Type Description
str

The title.

Source code in pydantic/json_schema.py
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
def get_title_from_name(self, name: str) -> str:
    """Retrieves a title from a name.

    Args:
        name: The name to retrieve a title from.

    Returns:
        The title.
    """
    return name.title().replace('_', ' ')

field_title_should_be_set

field_title_should_be_set(schema)

Returns true if a field with the given schema should have a title set based on the field name.

Intuitively, we want this to return true for schemas that wouldn't otherwise provide their own title (e.g., int, float, str), and false for those that would (e.g., BaseModel subclasses).

Parameters:

Name Type Description Default
schema CoreSchemaOrField

The schema to check.

required

Returns:

Type Description
bool

True if the field should have a title set, False otherwise.

Source code in pydantic/json_schema.py
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
def field_title_should_be_set(self, schema: CoreSchemaOrField) -> bool:
    """Returns true if a field with the given schema should have a title set based on the field name.

    Intuitively, we want this to return true for schemas that wouldn't otherwise provide their own title
    (e.g., int, float, str), and false for those that would (e.g., BaseModel subclasses).

    Args:
        schema: The schema to check.

    Returns:
        `True` if the field should have a title set, `False` otherwise.
    """
    if _core_utils.is_core_schema_field(schema):
        if schema['type'] == 'computed-field':
            field_schema = schema['return_schema']
        else:
            field_schema = schema['schema']
        return self.field_title_should_be_set(field_schema)

    elif _core_utils.is_core_schema(schema):
        if schema.get('ref'):  # things with refs, such as models and enums, should not have titles set
            return False
        if schema['type'] in {'default', 'nullable', 'definitions'}:
            return self.field_title_should_be_set(schema['schema'])  # type: ignore[typeddict-item]
        if _core_utils.is_function_with_inner_schema(schema):
            return self.field_title_should_be_set(schema['schema'])
        if schema['type'] == 'definition-ref':
            # Referenced schemas should not have titles set for the same reason
            # schemas with refs should not
            return False
        return True  # anything else should have title set

    else:
        raise PydanticInvalidForJsonSchema(f'Unexpected schema type: schema={schema}')  # pragma: no cover

normalize_name

normalize_name(name)

Normalizes a name to be used as a key in a dictionary.

Parameters:

Name Type Description Default
name str

The name to normalize.

required

Returns:

Type Description
str

The normalized name.

Source code in pydantic/json_schema.py
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
def normalize_name(self, name: str) -> str:
    """Normalizes a name to be used as a key in a dictionary.

    Args:
        name: The name to normalize.

    Returns:
        The normalized name.
    """
    return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name).replace('.', '__')

get_defs_ref

get_defs_ref(core_mode_ref)

Override this method to change the way that definitions keys are generated from a core reference.

Parameters:

Name Type Description Default
core_mode_ref CoreModeRef

The core reference.

required

Returns:

Type Description
DefsRef

The definitions key.

Source code in pydantic/json_schema.py
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
def get_defs_ref(self, core_mode_ref: CoreModeRef) -> DefsRef:
    """Override this method to change the way that definitions keys are generated from a core reference.

    Args:
        core_mode_ref: The core reference.

    Returns:
        The definitions key.
    """
    # Split the core ref into "components"; generic origins and arguments are each separate components
    core_ref, mode = core_mode_ref
    components = re.split(r'([\][,])', core_ref)
    # Remove IDs from each component
    components = [x.split(':')[0] for x in components]
    core_ref_no_id = ''.join(components)
    # Remove everything before the last period from each "component"
    components = [re.sub(r'(?:[^.[\]]+\.)+((?:[^.[\]]+))', r'\1', x) for x in components]
    short_ref = ''.join(components)

    mode_title = _MODE_TITLE_MAPPING[mode]

    # It is important that the generated defs_ref values be such that at least one choice will not
    # be generated for any other core_ref. Currently, this should be the case because we include
    # the id of the source type in the core_ref
    name = DefsRef(self.normalize_name(short_ref))
    name_mode = DefsRef(self.normalize_name(short_ref) + f'-{mode_title}')
    module_qualname = DefsRef(self.normalize_name(core_ref_no_id))
    module_qualname_mode = DefsRef(f'{module_qualname}-{mode_title}')
    module_qualname_id = DefsRef(self.normalize_name(core_ref))
    occurrence_index = self._collision_index.get(module_qualname_id)
    if occurrence_index is None:
        self._collision_counter[module_qualname] += 1
        occurrence_index = self._collision_index[module_qualname_id] = self._collision_counter[module_qualname]

    module_qualname_occurrence = DefsRef(f'{module_qualname}__{occurrence_index}')
    module_qualname_occurrence_mode = DefsRef(f'{module_qualname_mode}__{occurrence_index}')

    self._prioritized_defsref_choices[module_qualname_occurrence_mode] = [
        name,
        name_mode,
        module_qualname,
        module_qualname_mode,
        module_qualname_occurrence,
        module_qualname_occurrence_mode,
    ]

    return module_qualname_occurrence_mode

get_cache_defs_ref_schema

get_cache_defs_ref_schema(core_ref)

This method wraps the get_defs_ref method with some cache-lookup/population logic, and returns both the produced defs_ref and the JSON schema that will refer to the right definition.

Parameters:

Name Type Description Default
core_ref CoreRef

The core reference to get the definitions reference for.

required

Returns:

Type Description
tuple[DefsRef, JsonSchemaValue]

A tuple of the definitions reference and the JSON schema that will refer to it.

Source code in pydantic/json_schema.py
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
def get_cache_defs_ref_schema(self, core_ref: CoreRef) -> tuple[DefsRef, JsonSchemaValue]:
    """This method wraps the get_defs_ref method with some cache-lookup/population logic,
    and returns both the produced defs_ref and the JSON schema that will refer to the right definition.

    Args:
        core_ref: The core reference to get the definitions reference for.

    Returns:
        A tuple of the definitions reference and the JSON schema that will refer to it.
    """
    core_mode_ref = (core_ref, self.mode)
    maybe_defs_ref = self.core_to_defs_refs.get(core_mode_ref)
    if maybe_defs_ref is not None:
        json_ref = self.core_to_json_refs[core_mode_ref]
        return maybe_defs_ref, {'$ref': json_ref}

    defs_ref = self.get_defs_ref(core_mode_ref)

    # populate the ref translation mappings
    self.core_to_defs_refs[core_mode_ref] = defs_ref
    self.defs_to_core_refs[defs_ref] = core_mode_ref

    json_ref = JsonRef(self.ref_template.format(model=defs_ref))
    self.core_to_json_refs[core_mode_ref] = json_ref
    self.json_to_defs_refs[json_ref] = defs_ref
    ref_json_schema = {'$ref': json_ref}
    return defs_ref, ref_json_schema

handle_ref_overrides

handle_ref_overrides(json_schema)

It is not valid for a schema with a top-level $ref to have sibling keys.

During our own schema generation, we treat sibling keys as overrides to the referenced schema, but this is not how the official JSON schema spec works.

Because of this, we first remove any sibling keys that are redundant with the referenced schema, then if any remain, we transform the schema from a top-level '$ref' to use allOf to move the $ref out of the top level. (See bottom of https://swagger.io/docs/specification/using-ref/ for a reference about this behavior)

Source code in pydantic/json_schema.py
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
def handle_ref_overrides(self, json_schema: JsonSchemaValue) -> JsonSchemaValue:
    """It is not valid for a schema with a top-level $ref to have sibling keys.

    During our own schema generation, we treat sibling keys as overrides to the referenced schema,
    but this is not how the official JSON schema spec works.

    Because of this, we first remove any sibling keys that are redundant with the referenced schema, then if
    any remain, we transform the schema from a top-level '$ref' to use allOf to move the $ref out of the top level.
    (See bottom of https://swagger.io/docs/specification/using-ref/ for a reference about this behavior)
    """
    if '$ref' in json_schema:
        # prevent modifications to the input; this copy may be safe to drop if there is significant overhead
        json_schema = json_schema.copy()

        referenced_json_schema = self.get_schema_from_definitions(JsonRef(json_schema['$ref']))
        if referenced_json_schema is None:
            # This can happen when building schemas for models with not-yet-defined references.
            # It may be a good idea to do a recursive pass at the end of the generation to remove
            # any redundant override keys.
            if len(json_schema) > 1:
                # Make it an allOf to at least resolve the sibling keys issue
                json_schema = json_schema.copy()
                json_schema.setdefault('allOf', [])
                json_schema['allOf'].append({'$ref': json_schema['$ref']})
                del json_schema['$ref']

            return json_schema
        for k, v in list(json_schema.items()):
            if k == '$ref':
                continue
            if k in referenced_json_schema and referenced_json_schema[k] == v:
                del json_schema[k]  # redundant key
        if len(json_schema) > 1:
            # There is a remaining "override" key, so we need to move $ref out of the top level
            json_ref = JsonRef(json_schema['$ref'])
            del json_schema['$ref']
            assert 'allOf' not in json_schema  # this should never happen, but just in case
            json_schema['allOf'] = [{'$ref': json_ref}]

    return json_schema

encode_default

encode_default(dft)

Encode a default value to a JSON-serializable value.

This is used to encode default values for fields in the generated JSON schema.

Parameters:

Name Type Description Default
dft Any

The default value to encode.

required

Returns:

Type Description
Any

The encoded default value.

Source code in pydantic/json_schema.py
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
def encode_default(self, dft: Any) -> Any:
    """Encode a default value to a JSON-serializable value.

    This is used to encode default values for fields in the generated JSON schema.

    Args:
        dft: The default value to encode.

    Returns:
        The encoded default value.
    """
    return pydantic_core.to_jsonable_python(dft)

update_with_validations

update_with_validations(json_schema, core_schema, mapping)

Update the json_schema with the corresponding validations specified in the core_schema, using the provided mapping to translate keys in core_schema to the appropriate keys for a JSON schema.

Parameters:

Name Type Description Default
json_schema JsonSchemaValue

The JSON schema to update.

required
core_schema CoreSchema

The core schema to get the validations from.

required
mapping dict[str, str]

A mapping from core_schema attribute names to the corresponding JSON schema attribute names.

required
Source code in pydantic/json_schema.py
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
def update_with_validations(
    self, json_schema: JsonSchemaValue, core_schema: CoreSchema, mapping: dict[str, str]
) -> None:
    """Update the json_schema with the corresponding validations specified in the core_schema,
    using the provided mapping to translate keys in core_schema to the appropriate keys for a JSON schema.

    Args:
        json_schema: The JSON schema to update.
        core_schema: The core schema to get the validations from.
        mapping: A mapping from core_schema attribute names to the corresponding JSON schema attribute names.
    """
    for core_key, json_schema_key in mapping.items():
        if core_key in core_schema:
            json_schema[json_schema_key] = core_schema[core_key]

get_json_ref_counts

get_json_ref_counts(json_schema)

Get all values corresponding to the key '$ref' anywhere in the json_schema.

Source code in pydantic/json_schema.py
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
def get_json_ref_counts(self, json_schema: JsonSchemaValue) -> dict[JsonRef, int]:
    """Get all values corresponding to the key '$ref' anywhere in the json_schema."""
    json_refs: dict[JsonRef, int] = Counter()

    def _add_json_refs(schema: Any) -> None:
        if isinstance(schema, dict):
            if '$ref' in schema:
                json_ref = JsonRef(schema['$ref'])
                if not isinstance(json_ref, str):
                    return  # in this case, '$ref' might have been the name of a property
                already_visited = json_ref in json_refs
                json_refs[json_ref] += 1
                if already_visited:
                    return  # prevent recursion on a definition that was already visited
                defs_ref = self.json_to_defs_refs[json_ref]
                if defs_ref in self._core_defs_invalid_for_json_schema:
                    raise self._core_defs_invalid_for_json_schema[defs_ref]
                _add_json_refs(self.definitions[defs_ref])

            for v in schema.values():
                _add_json_refs(v)
        elif isinstance(schema, list):
            for v in schema:
                _add_json_refs(v)

    _add_json_refs(json_schema)
    return json_refs

emit_warning

emit_warning(kind, detail)

This method simply emits PydanticJsonSchemaWarnings based on handling in the warning_message method.

Source code in pydantic/json_schema.py
2043
2044
2045
2046
2047
def emit_warning(self, kind: JsonSchemaWarningKind, detail: str) -> None:
    """This method simply emits PydanticJsonSchemaWarnings based on handling in the `warning_message` method."""
    message = self.render_warning_message(kind, detail)
    if message is not None:
        warnings.warn(message, PydanticJsonSchemaWarning)

render_warning_message

render_warning_message(kind, detail)

This method is responsible for ignoring warnings as desired, and for formatting the warning messages.

You can override the value of ignored_warning_kinds in a subclass of GenerateJsonSchema to modify what warnings are generated. If you want more control, you can override this method; just return None in situations where you don't want warnings to be emitted.

Parameters:

Name Type Description Default
kind JsonSchemaWarningKind

The kind of warning to render. It can be one of the following:

  • 'skipped-choice': A choice field was skipped because it had no valid choices.
  • 'non-serializable-default': A default value was skipped because it was not JSON-serializable.
required
detail str

A string with additional details about the warning.

required

Returns:

Type Description
str | None

The formatted warning message, or None if no warning should be emitted.

Source code in pydantic/json_schema.py
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
def render_warning_message(self, kind: JsonSchemaWarningKind, detail: str) -> str | None:
    """This method is responsible for ignoring warnings as desired, and for formatting the warning messages.

    You can override the value of `ignored_warning_kinds` in a subclass of GenerateJsonSchema
    to modify what warnings are generated. If you want more control, you can override this method;
    just return None in situations where you don't want warnings to be emitted.

    Args:
        kind: The kind of warning to render. It can be one of the following:

            - 'skipped-choice': A choice field was skipped because it had no valid choices.
            - 'non-serializable-default': A default value was skipped because it was not JSON-serializable.
        detail: A string with additional details about the warning.

    Returns:
        The formatted warning message, or `None` if no warning should be emitted.
    """
    if kind in self.ignored_warning_kinds:
        return None
    return f'{detail} [{kind}]'

WithJsonSchema dataclass

Add this as an annotation on a field to override the (base) JSON schema that would be generated for that field. This provides a way to set a JSON schema for types that would otherwise raise errors when producing a JSON schema, such as Callable, or types that have an is-instance core schema, without needing to go so far as creating a custom subclass of pydantic.json_schema.GenerateJsonSchema. Note that any modifications to the schema that would normally be made (such as setting the title for model fields) will still be performed.

If mode is set this will only apply to that schema generation mode, allowing you to set different json schemas for validation and serialization.

Examples dataclass

Add examples to a JSON schema.

Examples should be a map of example names (strings) to example values (any valid JSON).

If mode is set this will only apply to that schema generation mode, allowing you to add different examples for validation and serialization.

SkipJsonSchema dataclass

Add this as an annotation on a field to skip generating a JSON schema for that field.

Example
from pydantic import BaseModel
from pydantic.json_schema import SkipJsonSchema

class Model(BaseModel):
    a: int | SkipJsonSchema[None] = None


print(Model.model_json_schema())
#> {'properties': {'a': {'default': None, 'title': 'A', 'type': 'integer'}}, 'title': 'Model', 'type': 'object'}

update_json_schema

update_json_schema(schema, updates)

Update a JSON schema by providing a dictionary of updates.

This function sets the provided key-value pairs in the schema and returns the updated schema.

Parameters:

Name Type Description Default
schema JsonSchemaValue

The JSON schema to update.

required
updates dict[str, Any]

A dictionary of key-value pairs to set in the schema.

required

Returns:

Type Description
JsonSchemaValue

The updated JSON schema.

Source code in pydantic/json_schema.py
81
82
83
84
85
86
87
88
89
90
91
92
93
94
def update_json_schema(schema: JsonSchemaValue, updates: dict[str, Any]) -> JsonSchemaValue:
    """Update a JSON schema by providing a dictionary of updates.

    This function sets the provided key-value pairs in the schema and returns the updated schema.

    Args:
        schema: The JSON schema to update.
        updates: A dictionary of key-value pairs to set in the schema.

    Returns:
        The updated JSON schema.
    """
    schema.update(updates)
    return schema

model_json_schema

model_json_schema(
    cls,
    by_alias=True,
    ref_template=DEFAULT_REF_TEMPLATE,
    schema_generator=GenerateJsonSchema,
    mode="validation",
)

Utility function to generate a JSON Schema for a model.

Parameters:

Name Type Description Default
cls type[BaseModel] | type[PydanticDataclass]

The model class to generate a JSON Schema for.

required
by_alias bool

If True (the default), fields will be serialized according to their alias. If False, fields will be serialized according to their attribute name.

True
ref_template str

The template to use for generating JSON Schema references.

DEFAULT_REF_TEMPLATE
schema_generator type[GenerateJsonSchema]

The class to use for generating the JSON Schema.

GenerateJsonSchema
mode JsonSchemaMode

The mode to use for generating the JSON Schema. It can be one of the following:

  • 'validation': Generate a JSON Schema for validating data.
  • 'serialization': Generate a JSON Schema for serializing data.
'validation'

Returns:

Type Description
dict[str, Any]

The generated JSON Schema.

Source code in pydantic/json_schema.py
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
def model_json_schema(
    cls: type[BaseModel] | type[PydanticDataclass],
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
    """Utility function to generate a JSON Schema for a model.

    Args:
        cls: The model class to generate a JSON Schema for.
        by_alias: If `True` (the default), fields will be serialized according to their alias.
            If `False`, fields will be serialized according to their attribute name.
        ref_template: The template to use for generating JSON Schema references.
        schema_generator: The class to use for generating the JSON Schema.
        mode: The mode to use for generating the JSON Schema. It can be one of the following:

            - 'validation': Generate a JSON Schema for validating data.
            - 'serialization': Generate a JSON Schema for serializing data.

    Returns:
        The generated JSON Schema.
    """
    schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
    if isinstance(cls.__pydantic_validator__, _mock_val_ser.MockValSer):
        cls.__pydantic_validator__.rebuild()
    assert '__pydantic_core_schema__' in cls.__dict__, 'this is a bug! please report it'
    return schema_generator_instance.generate(cls.__pydantic_core_schema__, mode=mode)

models_json_schema

models_json_schema(
    models,
    *,
    by_alias=True,
    title=None,
    description=None,
    ref_template=DEFAULT_REF_TEMPLATE,
    schema_generator=GenerateJsonSchema
)

Utility function to generate a JSON Schema for multiple models.

Parameters:

Name Type Description Default
models Sequence[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode]]

A sequence of tuples of the form (model, mode).

required
by_alias bool

Whether field aliases should be used as keys in the generated JSON Schema.

True
title str | None

The title of the generated JSON Schema.

None
description str | None

The description of the generated JSON Schema.

None
ref_template str

The reference template to use for generating JSON Schema references.

DEFAULT_REF_TEMPLATE
schema_generator type[GenerateJsonSchema]

The schema generator to use for generating the JSON Schema.

GenerateJsonSchema

Returns:

Type Description
tuple[dict[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]

A tuple where: - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have JsonRef references to definitions that are defined in the second returned element.) - The second element is a JSON schema containing all definitions referenced in the first returned element, along with the optional title and description keys.

Source code in pydantic/json_schema.py
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
def models_json_schema(
    models: Sequence[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode]],
    *,
    by_alias: bool = True,
    title: str | None = None,
    description: str | None = None,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
) -> tuple[dict[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]:
    """Utility function to generate a JSON Schema for multiple models.

    Args:
        models: A sequence of tuples of the form (model, mode).
        by_alias: Whether field aliases should be used as keys in the generated JSON Schema.
        title: The title of the generated JSON Schema.
        description: The description of the generated JSON Schema.
        ref_template: The reference template to use for generating JSON Schema references.
        schema_generator: The schema generator to use for generating the JSON Schema.

    Returns:
        A tuple where:
            - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
                whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
                JsonRef references to definitions that are defined in the second returned element.)
            - The second element is a JSON schema containing all definitions referenced in the first returned
                    element, along with the optional title and description keys.
    """
    instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
    inputs = [(m, mode, m.__pydantic_core_schema__) for m, mode in models]
    json_schemas_map, definitions = instance.generate_definitions(inputs)

    json_schema: dict[str, Any] = {}
    if definitions:
        json_schema['$defs'] = definitions
    if title:
        json_schema['title'] = title
    if description:
        json_schema['description'] = description

    return json_schemas_map, json_schema